Supernovae help astronomers measure how the Universe expands over time, but correctly interpreting this signal is far from straightforward: the light that reaches us is affected by multiple factors, making truly precise measurements extremely difficult. In a new study published in Nature Astronomy, Konstantin Karchev and Roberto Trotta of SISSA, together with Raúl Jiménez of the University of Barcelona, introduce CIGaRS (Combined Inference and Galaxy-Related Standardisation), a method based on artificial intelligence and neural networks that disentangles the intrinsic effects on the luminosity of these stellar explosions from environmental ones, such as interstellar dust and the expansion of the Universe. This makes it possible to extract far more information without requiring additional data, such as spectroscopic analyses.
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